
"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



def 						lowerCamelCase__      (				UpperCAmelCase_=None		,   UpperCAmelCase_=None					)->				Dict:







       """simple docstring"""
       return field(default_factory=lambda: default		,   metadata=UpperCAmelCase_					)



@dataclass
class 	__a	:
       UpperCamelCase_		:       List[str]				=      list_field(
           default=[]     ,  metadata={
               '''help''': (
                   '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
                   ''' of all available models'''
               )
           }     ,  )

       UpperCamelCase_		:       List[int]				=      list_field(
           default=[8]     ,  metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}						)

       UpperCamelCase_		:       List[int]				=      list_field(
           default=[8, 32, 128, 512]     ,  metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}     ,  )

       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}     ,  )
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}     ,  )
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Use FP16 to accelerate inference.'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Benchmark training of model'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Verbose memory tracing'''}						)
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}     ,  )
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
           }     ,  )
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Trace memory line by line'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Save result to a CSV file'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Save all print statements in a log file'''}						)
       UpperCamelCase_		:       bool				=      field(default=_lowerCAmelCase     ,  metadata={'''help''': '''Whether to print environment information'''}						)
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': (
                   '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
                   ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
                   ''' for debugging / testing and on TPU.'''
               )
           }     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""inference_time_{round(time()						)}.csv"""     ,  metadata={'''help''': '''CSV filename used if saving time results to csv.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""inference_memory_{round(time()						)}.csv"""     ,  metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""train_time_{round(time()						)}.csv"""     ,  metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""train_memory_{round(time()						)}.csv"""     ,  metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""env_info_{round(time()						)}.csv"""     ,  metadata={'''help''': '''CSV filename used if saving environment information.'''}     ,  )
       UpperCamelCase_		:       str				=      field(
           default=f"""log_{round(time()						)}.csv"""     ,  metadata={'''help''': '''Log filename used if print statements are saved in log.'''}     ,  )
       UpperCamelCase_		:       int				=      field(default=3     ,  metadata={'''help''': '''Times an experiment will be run.'''}						)
       UpperCamelCase_		:       bool				=      field(
           default=_lowerCAmelCase     ,  metadata={
               '''help''': (
                   '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
                   ''' model weights.'''
               )
           }     ,  )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Any:







              """simple docstring"""
              warnings.warn(
                  f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
                  " are deprecated in general and it is advised to use external Benchmarking libraries "
                  " to benchmark Transformer models."							,   UpperCAmelCase_							,   )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[int]:







              """simple docstring"""
              return json.dumps(dataclasses.asdict(self      )							,   indent=2      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	List[str]:







              """simple docstring"""
              if len(self.models      ) <= 0:
                     raise ValueError(
                         "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
                         " bert-base-cased` or `args.models = ['bert-base-cased']."      )
              return self.models






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	Union[str, Any]:







              """simple docstring"""
              if not self.multi_process:
                     return False
              elif self.is_tpu:
                     logger.info("Multiprocessing is currently not possible on TPU."      )
                     return False
              else:
                     return True







"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class 	__a	(   _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Optional[int]				=      LongformerTokenizer
       UpperCamelCase_		:       str				=      True
       UpperCamelCase_		:       Tuple				=      LongformerTokenizerFast
       UpperCamelCase_		:       List[str]				=      True






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Dict      )->	Any:







              """simple docstring"""
              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase       			=		[
                  "l",
                  "o",
                  "w",
                  "e",
                  "r",
                  "s",
                  "t",
                  "i",
                  "d",
                  "n",
                  "\u0120",
                  "\u0120l",
                  "\u0120n",
                  "\u0120lo",
                  "\u0120low",
                  "er",
                  "\u0120lowest",
                  "\u0120newer",
                  "\u0120wider",
                  "<unk>",
              ]
              UpperCamelCase       			=		dict(zip(UpperCAmelCase_							,   range(len(UpperCAmelCase_      )      )      )      )
              UpperCamelCase       			=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
              UpperCamelCase       			=		{"unk_token": "<unk>"}

              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["vocab_file"]      )
              UpperCamelCase       			=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["merges_file"]      )
              with open(self.vocab_file							,   "w"							,   encoding="utf-8"      ) as fp:
                     fp.write(json.dumps(UpperCAmelCase_      ) + "\n"      )
              with open(self.merges_file							,   "w"							,   encoding="utf-8"      ) as fp:
                     fp.write("\n".join(UpperCAmelCase_      )      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   **UpperCAmelCase_			:				Dict      )->	int:







              """simple docstring"""
              kwargs.update(self.special_tokens_map      )
              return self.tokenizer_class.from_pretrained(self.tmpdirname							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				str							,   **UpperCAmelCase_			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map      )
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Dict      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		"lower newer"
              UpperCamelCase       			=		"lower newer"
              return input_text, output_text






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer_class(self.vocab_file							,   self.merges_file							,   **self.special_tokens_map      )
              UpperCamelCase       			=		"lower newer"
              UpperCamelCase       			=		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
              UpperCamelCase       			=		tokenizer.tokenize(UpperCAmelCase_      )  # , add_prefix_space=True)
              self.assertListEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokens + [tokenizer.unk_token]
              UpperCamelCase       			=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		self.get_tokenizer()

              self.assertListEqual(tokenizer.encode("Hello world!"							,   add_special_tokens=UpperCAmelCase_      )							,   [0, 31_414, 232, 328, 2]      )
              self.assertListEqual(
                  tokenizer.encode("Hello world! cécé herlolip 418"							,   add_special_tokens=UpperCAmelCase_      )							,   [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]							,   )






       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"      )

              UpperCamelCase       			=		tokenizer.encode("sequence builders"							,   add_special_tokens=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.encode("multi-sequence build"							,   add_special_tokens=UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(
                  "sequence builders"							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.encode(
                  "sequence builders"							,   "multi-sequence build"							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_							,   UpperCAmelCase_      )

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		self.get_tokenizer()

              UpperCamelCase       			=		"Encode this sequence."
              UpperCamelCase       			=		tokenizer.byte_encoder[" ".encode("utf-8"      )[0]]

              # Testing encoder arguments
              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[0]      )[0]
              self.assertEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              tokenizer.add_special_tokens({"bos_token": "<s>"}      )
              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[1]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              # Testing spaces after special tokens
              UpperCamelCase       			=		"<mask>"
              tokenizer.add_special_tokens(
                  {"mask_token": AddedToken(UpperCAmelCase_							,   lstrip=UpperCAmelCase_							,   rstrip=UpperCAmelCase_      )}      )  # mask token has a left space
              UpperCamelCase       			=		tokenizer.convert_tokens_to_ids(UpperCAmelCase_      )

              UpperCamelCase       			=		"Encode <mask> sequence"
              UpperCamelCase       			=		"Encode <mask>sequence"

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_      )
              UpperCamelCase       			=		encoded.index(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertEqual(UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		tokenizer.encode(UpperCAmelCase_      )
              UpperCamelCase       			=		encoded.index(UpperCAmelCase_      )
              UpperCamelCase       			=		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]      )[0]
              self.assertNotEqual(UpperCAmelCase_							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
                            UpperCamelCase       			=		self.tokenizer_class.from_pretrained(UpperCAmelCase_							,   **UpperCAmelCase_      )
                            UpperCamelCase       			=		"A, <mask> AllenNLP sentence."
                            UpperCamelCase       			=		tokenizer_r.encode_plus(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_p.encode_plus(UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_							,   return_token_type_ids=UpperCAmelCase_      )

                            # token_type_ids should put 0 everywhere
                            self.assertEqual(sum(tokens_r["token_type_ids"]      )							,   sum(tokens_p["token_type_ids"]      )      )

                            # attention_mask should put 1 everywhere, so sum over length should be 1
                            self.assertEqual(
                                sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )							,   sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )							,   )

                            UpperCamelCase       			=		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
                            UpperCamelCase       			=		tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

                            # Rust correctly handles the space before the mask while python doesnt
                            self.assertSequenceEqual(tokens_p["input_ids"]							,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
                            self.assertSequenceEqual(tokens_r["input_ids"]							,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )

                            self.assertSequenceEqual(
                                UpperCAmelCase_							,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
                            self.assertSequenceEqual(
                                UpperCAmelCase_							,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	Dict:







              """simple docstring"""
              for trim_offsets, add_prefix_space in itertools.product([True, False]							,   repeat=2      ):
                     UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )

                     UpperCamelCase       			=		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()      )
                     UpperCamelCase       			=		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()      )

                     self.assertEqual(pre_tokenizer_state["add_prefix_space"]							,   UpperCAmelCase_      )

                     self.assertEqual(post_processor_state["add_prefix_space"]							,   UpperCAmelCase_      )
                     self.assertEqual(post_processor_state["trim_offsets"]							,   UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	Union[str, Any]:







              """simple docstring"""
              # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
              # `trim_offsets`
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
                            UpperCamelCase       			=		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                            UpperCamelCase       			=		f"{text_of_1_token} {text_of_1_token}"

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ) + 1, len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ) + 1, len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ), len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (len(UpperCAmelCase_      ), len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		f" {text}"

                            # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                            #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                            # )
                            # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                            # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                            # self.assertEqual(
                            #     encoding.offset_mapping[1],
                            #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                            # )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (1, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ) + 1, 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ), 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )

                            UpperCamelCase       			=		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_							,   use_fast=UpperCAmelCase_							,   add_prefix_space=UpperCAmelCase_							,   trim_offsets=UpperCAmelCase_      )
                            UpperCamelCase       			=		tokenizer_r(UpperCAmelCase_							,   return_offsets_mapping=UpperCAmelCase_							,   add_special_tokens=UpperCAmelCase_      )
                            self.assertEqual(encoding.offset_mapping[0]							,   (0, 1 + len(UpperCAmelCase_      ))      )
                            self.assertEqual(
                                encoding.offset_mapping[1]							,   (1 + len(UpperCAmelCase_      ), 1 + len(UpperCAmelCase_      ) + 1 + len(UpperCAmelCase_      ))							,   )





